Learn programming from scratch with Python

keywords (reserved words in Python) and identifiers (names given to variables, functions, etc.)

Python statements, why indentation is important and use of comments in programming

A docstring is short for documentation string.

Python docstrings (documentation strings) are the string literals that appear right after the definition of a function, method, class, or module

In this lecture, you will learn about Python variables, constants, and their use cases

A variable is a named location used to store data in the memory. It is helpful to think of variables as a container that holds data that can be changed later in the program.

A constant is a type of variable whose value cannot be changed. It is helpful to think of constants as containers that hold information which cannot be changed later

Literal is a raw data given in a variable or constant. In Python, there are various types of literals:

Numeric Literals are immutable (unchangeable). Numeric literals can belong to 3 different numerical types: Integer, Float, and Complex.

A string literal is a sequence of characters surrounded by quotes. We can use both single, double, or triple quotes for a string.

A Boolean literal can have any of the two values: True or False.

Python contains one special literal i.e. None. We use it to specify that the field has not been created.

There are four different literal collections List literals, Tuple literals, Dict literals, and Set literals.

In this lecture, you will learn about different data types you can use in Python.

Every value in Python has a datatype. Since everything is an object in Python programming, data types are actually classes and variables are instance (object) of these classes

In this lecture, you will learn about the Type conversions and uses of type conversions.

The process of converting the value of one data type (integer, string, float, etc.) to another data type is called type conversion. Python has two types of type conversion.

1. Implicit Type Conversion

2. Explicit Type Conversion

In Implicit type conversion, Python automatically converts one data type to another data type. This process doesn't need any user involvement.

In Explicit Type Conversion, users convert the data type of an object to required data type. We use the predefined functions like int(), float(), str(), etc to perform explicit type conversion

This lecture focuses on two built-in functions print() and input() to perform I/O tasks in Python.

You will also learn to import modules and use them in your program.

In this lecture, you'll learn everything about different types of operators in Python, their syntax and how to use them with examples.

Operators are special symbols in Python that carry out arithmetic or logical computation. The value that the operator operates on is called the operand

In this lecture, you will learn about namespaces, mapping from names to objects, and scope of a variable.

Name (also called identifier) is simply a name given to objects. Everything in Python is an object. Name is a way to access the underlying object.

A namespace is a collection of names.

In this lecture, you will learn to create decisions in a Python program using different forms of if..else statement

In this lecture, you'll learn to iterate over a sequence of elements using the different variations of for loop

Loops are used in programming to repeat a specific block of code. In this lecture, you will learn to create a while loop in Python

In this lecture, you will learn to use break and continue statements to alter the flow of a loop

In this lecture, you'll learn about pass statement. It is used as a placeholder for future implementation of functions, loops, etc.

In this lecture, you'll learn about functions, what a function is, the syntax, components, and types of functions. Also, you'll learn to create a function in Python

In Python, an anonymous function is a function that is defined without a name.

While normal functions are defined using the def keyword in Python, anonymous functions are defined using the lambda keyword.

In this lecture, you’ll learn about Python Global variables, Local variables, Nonlocal variables and where to use them.

In this lecture, you’ll learn about the global keyword, global variable and when to use global keywords.

In this lecture, you will learn to create and import custom modules in Python. Also, you will find different techniques to import and use custom and built-in modules in Python.

Modules refer to a file containing Python statements and definitions.

In this lecture, you'll learn to divide your code base into clean, efficient modules using Python packages. Also, you'll learn to import and use your own or third party packages in your Python program.

In this lecture, you'll learn about the different numbers used in Python, how to convert from one data type to the other, and the mathematical operations supported in Python.

Python supports integers, floating-point numbers and complex numbers. They are defined as int, float, and complex classes in Python.

In this and next lectures, you'll learn everything about Python lists, how they are created, slicing of a list, adding or removing elements from them and so on.

Python offers a range of compound data types often referred to as sequences. List is one of the most frequently used and very versatile data types used in Python.

In this and next lectures, you'll learn everything about Python tuples. More specifically, what are tuples, how to create them, when to use them and various methods you should be familiar with.

A tuple in Python is similar to a list. The difference between the two is that we cannot change the elements of a tuple once it is assigned whereas we can change the elements of a list.

In this and next lectures you will learn to create, format, modify and delete strings (a string is a sequence of characters) in Python. Also, you will be introduced to various string operations and functions.

In this and next lecture, you'll learn everything about Python sets; how they are created, adding or removing elements from them, and all operations performed on sets in Python.

A set is an unordered collection of items. Every set element is unique (no duplicates) and must be immutable (cannot be changed).

In this lecture, you'll learn everything about Python dictionaries; how they are created, accessing, adding, removing elements from them and various built-in methods.

Python dictionary is an unordered collection of items. Each item of a dictionary has a key/value pair.

In this and next lecture, you'll learn about Python file operations. More specifically, opening a file, reading from it, writing into it, closing it, and various file methods that you should be aware of.

In this lecture, you'll learn about file and directory management in Python, i.e. creating a directory, renaming it, listing all directories, and working with them.

In this lecture, you will learn about different types of errors and exceptions that are built-in to Python. They are raised whenever the Python interpreter encounters errors.

We can make certain mistakes while writing a program that lead to errors when we try to run it. A python program terminates as soon as it encounters an unhandled error. These errors can be broadly classified into two classes:

1. Syntax errors

2. Logical errors (Exceptions)

In this and next lectures, you'll learn how to handle exceptions in your Python program using try, except and finally statements with the help of examples.

In this lecture, you will learn how to define custom exceptions depending upon your requirements with the help of examples.

Python has numerous built-in exceptions that force your program to output an error when something in the program goes wrong.

However, sometimes you may need to create your own custom exceptions that serve your purpose.

In this lecture, you’ll learn about Object-Oriented Programming (OOP) in Python and its fundamental concept with the help of examples.

Python is a multi-paradigm programming language. It supports different programming approaches.

One of the popular approaches to solve a programming problem is by creating objects. This is known as Object-Oriented Programming (OOP).

An object has two characteristics:

• attributes

• behavior

In this and next lecture, you will learn about the core functionality of Python objects and classes. You'll learn what a class is, how to create it and use it in your program.

Python is an object oriented programming language. Unlike procedure oriented programming, where the main emphasis is on functions, object oriented programming stresses on objects.

An object is simply a collection of data (variables) and methods (functions) that act on those data. Similarly, a class is a blueprint for that object.

Inheritance enables us to define a class that takes all the functionality from a parent class and allows us to add more. In this tutorial, you will learn to use inheritance in Python.

Inheritance is a powerful feature in object oriented programming.

It refers to defining a new class with little or no modification to an existing class. The new class is called derived (or child) class and the one from which it inherits is called the base (or parent) class.

In this lecture, you'll learn about multiple inheritance in Python and how to use it in your program. You'll also learn about multi-level inheritance and the method resolution order.

A class can be derived from more than one base class in Python. This is called multiple inheritance.

In multiple inheritance, the features of all the base classes are inherited into the derived class.

Using OOP in Python, we can restrict access to methods and variables. This prevents data from direct modification which is called encapsulation.

Polymorphism is an ability (in OOP) to use a common interface for multiple forms (data types).

You can change the meaning of an operator in Python depending upon the operands used. In this and net lectures, you will learn how to use operator overloading in Python Object Oriented Programming.

Iterators are objects that can be iterated upon. In this and next lectures, you will learn how iterator works and how you can build your own iterator using __iter__ and __next__ methods.

In this lecture, you'll learn how to create iterations easily using Python generators, how it is different from iterators and normal functions, and why you should use it.

Python generators are a simple way of creating iterators

In this lecture, you'll learn about Python closure, how to define a closure, and the reasons you should use it.

A decorator takes in a function, adds some functionality and returns it. In this and next lectures, you will learn how you can create a decorator and why you should use it.

Python has an interesting feature called decorators to add functionality to an existing code.

In this and next lectures, you will learn about Python @property decorator; a pythonic way to use getters and setters in object-oriented programming.

Python programming provides us with a built-in @property decorator which makes usage of getter and setters much easier in Object-Oriented Programming.

In this lecture, you will learn about regular expressions (RegEx), and use Python's re module to work with RegEx (with the help of examples).

A Regular Expression (RegEx) is a sequence of characters that defines a search pattern.

Python provides built-in JSON libraries to encode and decode JSON.

There are two basic formats for JSON data. Either in a string or the object datastructure.

You can create partial functions in python by using the partial function from the functools library.

Partial functions allow one to derive a function with x parameters to a function with fewer parameters and fixed values set for the more limited function.

Code introspection is the ability to examine classes, functions and keywords to know what they are, what they do and what they know.

Python provides several functions and utilities for code introspection.

Map, Filter, and Reduce are important for functional programming. They allow the programmer to write simpler, shorter code, without neccessarily needing to bother about intricacies like loops and branching.

Essentially, these three functions allow you to apply a function across a number of iterables, in one full swoop. map and filter come built-in with Python (in the __builtins__ module) and require no importing. reduce, however, needs to be imported as it resides in the functools module. Let's get a better understanding of how they all work, starting with map.

In this section, you will learn to manipulate date and time in Python with the help of examples.

Python has a module named datetime to work with dates and times.

In this lecture, you will learn to convert date, time and datetime objects to its equivalent string (with the help of examples)

The strftime() method returns a string representing date and time using date, time or datetime object.

In this lecture, you will learn to create a datetime object from a string (with the help of examples).

The strptime() method creates a datetime object from the given string.

In this lecture, you will learn to get today's date and current date and time in Python. We will also format the date and time in different formats using strftime() method.

There are a number of ways you can take to get the current date. We will use the date class of the datetime module to accomplish this task.

In this lecture, you will learn to get current time of your locale.

There are a number of ways you can take to get current time in Python.

In this lecture, you will learn to convert timestamp to datetime object and datetime object to timestamp (with the help of examples).

It's pretty common to store date and time as a timestamp in a database.

In this lecture, we will explore time module in detail. We will learn to use different time-related functions defined in the time module with the help of examples.

Python has a module named time to handle time-related tasks. To use functions defined in the module, we need to import the module first.

The sleep() function suspends (waits) execution of the current thread for a given number of seconds.

Python has a module named time which provides several useful functions to handle time-related tasks. One of the popular functions among them is sleep().

The sleep() function suspends execution of the current thread for a given number of seconds.